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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/17399
Title: Local Directional Gradient Based Feature learning for Image Retrieval
Authors: Galshetwar G.M.
Patil P.W.
Gonde A.B.
Waghmare L.M.
Maheshwari R.P.
Published in: Proceedings of 2018 13th International Conference on Industrial and Information Systems, ICIIS 2018
Abstract: Content-based image retrieval (CBIR) became an energetic research field in engineering and medical field. In this work, local directional gradient based feature learning approach for image retrieval is proposed using artificial neural network. Initially, for a given reference pixel, first order derivatives in four different directions is calculated. Later maximum (top two) energy variations among calculated derivatives are considered to indicate maximum changes in those specific directions. Further, 3× 3 local reference grid and two 3× 3 local directional grids based on top two maximum magnitude directions are extracted. Finally, relationship among pixels of extracted 3× 3 local grids are encoded using triplet pattern. The complete procedure is named as directional magnitude local triplet pattern (DMLTriP). The retrieval accuracy is measured using two different technique i.e. traditional and learning based CBIR. The parameter like average retrieval precision (ARP) and average retrieval rate (ARR) are considered for image retrieval accuracy measurement on publicly available (natural and medical) image databases. All the experiments discussed in this article clearly shows that proposed feature descriptor outperforms existing state-of-the-art local feature descriptors. © 2018 IEEE.
Citation: Proceedings of 2018 13th International Conference on Industrial and Information Systems, ICIIS 2018, (2018), 113- 118
URI: https://doi.org/10.1109/ICIINFS.2018.8721437
http://repository.iitr.ac.in/handle/123456789/17399
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Artificial neural network
Gradient
Intensity progression
Local ternary pattern (LTP)
Matching
ISBN: 9781538616765
Author Scopus IDs: 57196261768
57204924942
26639267400
9338273200
8941720600
Author Affiliations: Galshetwar, G.M., Department of ECE, India
Patil, P.W., Department of ECE, India
Gonde, A.B., Department of ECE, India
Waghmare, L.M., Department of Instrumentation Engineering, SGGSIET Nanded (MS), India
Maheshwari, R.P., Department of Electrical Engineering, IIT Roorkee, India
Appears in Collections:Conference Publications [EE]

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